Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Demo: Assisting Visually Impaired People Navigate Indoors
Authors: J. Pablo Muñoz, Bing Li, Xuejian Rong, Jizhong Xiao, Yingli Tian, Aries Arditi
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this demo, we show a system that uses state-of the-art technology to assist visually impaired people navigate indoors. Our system takes advantage of spatial representations from CAD files, or floor plan images, to extract valuable information that later can be used to improve navigation and human-computer interaction. Using depth information, our system is capable of detecting obstacles and guiding the user to avoid them. |
| Researcher Affiliation | Collaboration | J. Pablo Mu noz City University of New York, NY EMAIL Bing Li, Xuejian Rong Jizhong Xiao, Yingli Tian City College of New York, NY EMAIL Aries Arditi Visibility Metrics Chappaqua, NY EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It provides mathematical equations but no algorithm steps. |
| Open Source Code | No | Videos of our system can be found at: http://www.assistiverobot.org/ain/. The paper provides a link to project videos, but does not explicitly state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | No | The paper refers to using data from 'CAD files, or floor plan images' and 'Google s Project Tango handheld device' which produces an 'Area Description File (ADF) file'. However, it does not provide concrete access information (link, DOI, formal citation) for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages or sample counts for training, validation, or testing. |
| Hardware Specification | Yes | Our system uses the localization capabilities of Google s Project Tango handheld device. |
| Software Dependencies | No | The paper mentions using 'well-known Artificial Intelligence algorithms, e.g., A*', and 'Voice-Recognition, Gesture Detection, and Text-to-Speech capabilities of handheld devices', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the functional components and logic of the system, but it does not provide specific experimental setup details such as hyperparameter values or training configurations. |